Publication
Recognition Techniques for Whiteboard Notes Written in Roman Script
Marcus Liwicki; Horst Bunke
In: C H Chen. Handbook of Pattern Recognition and Computer Vision. Chapter 3.5, Pages 397-414, ISBN 978-981-4273-38-1, World Scientific, 2010.
Abstract
In this chapter we describe various methods for the automatic recognition of handwritten
whiteboard notes. A handwriting recognition system for Roman Script
is usually divided into units which iteratively process the handwritten input data
to finally obtain the desired ASCII transcription: the preprocessing, where noise
in the raw data is reduced; the normalization, where various steps take place to
remove writer-specific characteristics of the handwriting; the feature extraction,
where the normalized data is transformed into a sequence of feature vectors; the
recognition, where a classifier generates a list of word sequence candidates; and
the post-processing, where language information is used to improve the results.
We review different approaches for all of these stages and describe selected approaches
in more detail. Furthermore, we introduce some preprocessing steps
which have been developed especially for whiteboard notes. In order to assess the
advantages of different methods, we present the results of a broad experimental
analysis on a large database of handwritten whiteboard notes.